Development of a label-free Raman imaging technique for differentiation of malaria parasite infected from non-infected tissue

Laura Frame, James Brewer, Rebecca Lee, Karen Faulds, Duncan Graham

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
14 Downloads (Pure)


During malarial infection, the host uses the spleen to clear the malaria parasites, however, the parasites have evolved the ability to bind to endothelial receptors in blood vessels of tissues to avoid removal, known as sequestration, and this is largely responsible for the symptoms and severity of infection. So a technique which could non-invasively diagnose tissue burden could be utilised as an aid for localised malaria diagnosis within tissue. Raman spectroscopy is a label-free imaging technique and can provide unique and chemically specific Raman ‘fingerprint’ spectrum of biological samples such as tissue. Within this study, Raman imaging was used to observe the changes to the molecular composition of mice spleen tissue under malarial infection, compared with non-infected samples. From analysis of the Raman imaging data, both tissue types showed very similar spectral profiles, which highlighted that their biochemical compositions were closely linked. Principal component analysis showed very clear separation of the two sample groups, with an associated increase in concentration of heme-based Raman vibrations within the infected dataset. This was indicative of the presence of hemozoin, the malaria pigment, being detected within the infected spleen. Separation also showed that as the hemozoin content within the tissue increased, there was a corresponding change to hemoglobin and some lipid/nucleic acid vibrations. These results demonstrate that Raman spectroscopy can be used to easily discriminate the subtle changes in tissue burden upon malarial infection.
Original languageEnglish
Pages (from-to)157-163
Number of pages7
Issue number1
Early online date16 Nov 2017
Publication statusPublished - 7 Jan 2018


  • malarial infection
  • malaria
  • Raman spectroscopy

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